The subject matter disclosed herein relates to unmanned aerial vehicles, and more particularly, to evaluating and/or improving target aiming accuracy for unmanned aerial vehicles.
Various entities may own or maintain different types of industrial assets as part of their operation. Such assets may include physical or mechanical devices or structures, which may, in some instances, utilize electrical and/or chemical technologies. Such assets may be used or maintained for a variety of purposes and may be characterized as capital infrastructure, inventory, or by other nomenclature depending on the context. For example, industrial assets may include distributed assets, such as a pipeline or an electrical grid, as well as individual or discrete assets, such as a wind turbine, airplane, a flare stack, vehicle, etc. Assets may be subject to various types of defects (e.g., spontaneous mechanical defects, electrical defects, or routine wear-and-tear) that may impact operation. For example, over time, an industrial asset may undergo corrosion or cracking due to weather or may exhibit deteriorating performance or efficiency due to the wear or failure of one or more component parts.
To look for potential defects, one or more aerial inspection robots equipped with sensors might be used to inspect an industrial asset. For example, a drone might be configured to fly in the proximity of an industrial flare stack taking pictures of various points of interest. As part of this process, an autonomous (or semi-autonomous) drone might follow a pre-determined flight path and/or make on-the-fly navigation decisions to collect data as appropriate. The collection of data will typically involve moving the drone to a pre-determined location and orienting an independently movable sensor (e.g., a camera) toward the point of interest. There can be errors, however, in both the location of the drone and the orientation of the sensor (e.g., flight control errors, gimbal sensor noise, etc.) that can result in the sensor pointing (or “target aiming”) to an incorrect location (e.g., a camera might be pointed 15 degrees away from an actual point of interest on an asset). This can reduce the usefulness of the asset inspection. This can be especially true when a planned inspection will take a substantial amount of time, the inspection can potentially take various routes, there are many points of interest to be examined, the asset and/or surrounding environment are complex and dynamically changing, sudden changes in the lighting or environmental conditions (e.g., a gust of wind), etc.
It would therefore be desirable to provide systems and methods to evaluate and/or improve target aiming accuracy in an automated and efficient manner.